Review of the Kalman-type hydrological data assimilation |
| |
| Authors: | Leqiang Sun Ousmane Seidou Ioan Nistor Kailei Liu |
| |
| Affiliation: | 1. Civil Engineering Department, University of Ottawa, Ottawa, Canadalsun034@uottawa.ca;3. Civil Engineering Department, University of Ottawa, Ottawa, Canada;4. College of Hydrology and Water Resources, Hohai University, Nanjing, China |
| |
| Abstract: | ABSTRACTThere is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalman-type DA but also provide an important reference to other hydrological DA types. Editor D. Koutsoyiannis; Associate editor F. Pappenberger |
| |
| Keywords: | data assimilation extended Kalman filter ensemble Kalman filter model error parameter estimation |
|
|
